BRIDGE²
Compiles portable skills into Gemini/Google agent artifacts, including SKILL.md and MCP configuration.
Compiles portable skills into Meta/Llama function schemas using neutral tools.json for function/tool calling.
Compiles portable skills into OpenAI/ChatGPT/Codex artifacts, including SKILL.md, MCP descriptor, and function schemas for tool calling.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@BRIDGE²Validate my skill.yaml and compile it for Claude and ChatGPT"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
D'AUBE // BRIDGE²
Write once. Run across agents.
D'AUBE // BRIDGE² is an open-source universal AI skill compiler and MCP bridge. Define one portable capability spec, then compile it into integration artifacts for ChatGPT/Codex, Claude, Gemini, DeepSeek, Meta/Llama, MCP hosts, and Chromium browsers.
Why BRIDGE²
AI tooling is fragmented. The same capability is repeatedly rewritten as MCP tools, provider function schemas, skills, plugins, and browser configuration. BRIDGE² treats those surfaces as compiler targets while keeping capability logic provider-neutral.
skill.yaml
|
+-- MCP tool schema
+-- OpenAI / ChatGPT / Codex artifacts
+-- Claude plugin + SKILL.md
+-- Gemini SKILL.md + MCP config
+-- DeepSeek tool schemas
+-- Meta/Llama function schemas
+-- Chromium extension manifestCurrent release: 7 target families, 15 deterministic artifacts, one MCP server.
Related MCP server: AI Skills MCP Server
30-second start
git clone https://github.com/daubesonntag-dotcom/daube-agent-bridge.git
cd daube-agent-bridge
python -m pip install -e ".[dev]"
daube-bridge validate examples/skill.yaml
daube-bridge compile examples/skill.yaml -o build/sampleInstall the verified v0.1.3 wheel directly from GitHub Releases:
python -m pip install https://github.com/daubesonntag-dotcom/daube-agent-bridge/releases/download/v0.1.3/daube_agent_bridge-0.1.3-py3-none-any.whl
python -m daube_bridge doctorpython -m daube_bridge ... is the shim-free CLI path for Windows environments where Application Control blocks generated console-script executables.
Run BRIDGE² as a local stdio MCP server:
python -m daube_bridge.serverOr expose the HTTP MCP endpoint for remote/local integrations:
daube-bridge serve
# http://127.0.0.1:8000/mcpOne-click MCPB
BRIDGE² ships an MCPB v0.4 bundle definition using the cross-platform uv runtime. Build the deterministic bundle with:
python scripts/build_mcpb.pyThe build is reproducible: identical source produces an identical archive hash. Official MCP Registry metadata lives in server.json.
Target matrix
Surface | Output | Strategy |
MCP hosts | MCP server + tool schemas | Shared transport/tool backbone |
OpenAI / ChatGPT / Codex |
| Skills + MCP/tool calling |
Claude |
| Plugin + portable skill + MCP |
Gemini / Google agents |
| Portable skill + MCP |
DeepSeek | OpenAI-style | Function/tool calling adapter |
Meta / Llama | neutral | Application/router function schema |
Chromium browsers | Manifest V3 side panel | Endpoint/config control surface |
This matrix describes integration artifacts BRIDGE² generates. It does not invent provider capabilities that do not exist.
Portable skill format
name: My Research Skill
version: 0.1.0
description: Research and compare options.
tools:
- name: compare_options
description: Compare options against explicit criteria.
parameters:
type: object
properties:
options:
type: array
items: {type: string}
required: [options]MCP tools
The bundled server exposes three small primitives:
bridge_targets— discover supported compilation targets.validate_skill— validate a portable skill object.compile_skill— compile a skill in-memory for another agent or tool.
That makes BRIDGE² usable both as a CLI and as infrastructure callable by other agents.
Verify before release
python scripts/verify.py
python scripts/build_mcpb.py
.tools/mcp-publisher.exe validateThe verification gate covers tests, Ruff, target discovery, sample compilation, wheel creation, deterministic MCPB creation, and official MCP Registry metadata validation.
After publishing a GitHub Release, smoke-test the public wheel in an isolated environment:
python scripts/smoke_release.py 0.1.3Roadmap
ChatGPT Apps SDK interactive widget target.
npx/ TypeScript compiler parity.Provider capability detection and compatibility linting.
Signed bundles and provenance manifests.
Remote registry + searchable skill catalog.
One-click adapters for agent IDEs and orchestration frameworks.
Golden interoperability fixtures across providers.
Project principles
Portable by default. Prefer MCP and open schemas over provider lock-in.
Evidence over pretending. Never claim native support that a provider does not have.
Readable outputs. Generated artifacts stay human-auditable and diffable.
Safe boundaries. Authentication and sensitive actions remain explicit.
Fast adoption. A useful first result should take minutes, not a framework migration.
Contributing
Issues, adapters, compatibility fixtures, docs, and provider integrations are welcome. Provider-format changes should include a link to authoritative documentation and a minimal reproducible fixture.
Useful starting points:
License
MIT © 2026 D'AUBE SONNTAG.
This server cannot be deployed
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